红外技术Issue(4)：283-288,6.
采用DCT稀疏表示与Dual-PCNN的图像融合算法
An Image Fusion Algorithm Based on DCT Sparse Representation and Dual-PCNN
摘要
Abstract
The existing image fusion method results in uneven image brightness, not agreeing with the original image contrast, not suitable for the human eye visual defects. To solve this problem, a new algorithm based on compressive sensing, which combined the DCT sparse representation with the Dual-channel pulse coupled neural network mode, is offered in this paper. First, for the character of the DCT sparse representation, Radial Sampling Matrix is designed. Second, the measurements based on the weighted average is fused with the information entropy of measurements. Finally, the total variation algorithm is used tore construct the fusion image. Experiments have been done to fuse multiple sets of different types of sensor image. Both subjective visual analysis and objective evaluation criteria show that the proposed algorithm can obtain more useful information from the original image, keep the edge information of original image, and get a better visual effect.关键词
压缩感知/双通道脉冲耦合神经网络/信息熵/全变分优化算法Key words
compressive sensing/dual-channel pulse coupled neural network/information entropy/the total variation algorithm分类
信息技术与安全科学引用本文复制引用
宋斌,吴乐华,唐晓杰,玉强,牟宇飞..采用DCT稀疏表示与Dual-PCNN的图像融合算法[J].红外技术,2015,(4):283-288,6.基金项目
重庆市基础与前沿研究计划项目，编号cstc2013jcyjA40045）；重庆市高校创新团队建设计划项目，编号KJTD201343。 （）
